



from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
    if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
    from .pipeline_unclip import UnCLIPPipeline
    from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
    from .text_proj import UnCLIPTextProjModel








import unittest

import numpy as np

from transformers import is_flax_available
from transformers.testing_utils import require_flax

from ..test_modeling_flax_common import ids_tensor


if is_flax_available():
    import jax
    import jax.numpy as jnp

    from transformers.generation import (
        FlaxForcedBOSTokenLogitsProcessor,
        FlaxForcedEOSTokenLogitsProcessor,
        FlaxLogitsProcessorList,
        FlaxMinLengthLogitsProcessor,
        FlaxTemperatureLogitsWarper,
        FlaxTopKLogitsWarper,
        FlaxTopPLogitsWarper,
    )



@require_flax
class     lowerCAmelCase_		(  unittest.TestCase							):
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		Union[str, Any]:
          UpperCamelCase   :  List[str]						     =     jnp.ones((batch_size, length)	) / length
          return scores
       def  snake_case_	(      self	)					->		Optional[Any]:
          UpperCamelCase   :  Optional[Any]						     =     None
          UpperCamelCase   :  Optional[int]						     =     20

          UpperCamelCase   :  Optional[Any]						     =     self._get_uniform_logits(batch_size=2,      length=SCREAMING_SNAKE_CASE_	)

          # tweak scores to not be uniform anymore
          UpperCamelCase   :  Dict						     =     scores.at[1, 5].set((1 / length) + 0.1	)  # peak, 1st batch
          UpperCamelCase   :  Any						     =     scores.at[1, 10].set((1 / length) - 0.4	)  # valley, 1st batch

          # compute softmax
          UpperCamelCase   :  List[str]						     =     jax.nn.softmax(SCREAMING_SNAKE_CASE_,      axis=-1	)

          UpperCamelCase   :  List[Any]						     =     FlaxTemperatureLogitsWarper(temperature=0.5	)
          UpperCamelCase   :  int						     =     FlaxTemperatureLogitsWarper(temperature=1.3	)

          UpperCamelCase   :  Tuple						     =     jax.nn.softmax(temp_dist_warper_sharper(SCREAMING_SNAKE_CASE_,      scores.copy(),      cur_len=SCREAMING_SNAKE_CASE_	),      axis=-1	)
          UpperCamelCase   :  Any						     =     jax.nn.softmax(temp_dist_warper_smoother(SCREAMING_SNAKE_CASE_,      scores.copy(),      cur_len=SCREAMING_SNAKE_CASE_	),      axis=-1	)

          # uniform distribution stays uniform
          self.assertTrue(jnp.allclose(probs[0, :],      warped_prob_sharp[0, :],      atol=1e-3	)	)
          self.assertTrue(jnp.allclose(probs[0, :],      warped_prob_smooth[0, :],      atol=1e-3	)	)

          # sharp peaks get higher, valleys get lower
          self.assertLess(probs[1, :].max(),      warped_prob_sharp[1, :].max()	)
          self.assertGreater(probs[1, :].min(),      warped_prob_sharp[1, :].min()	)

          # smooth peaks get lower, valleys get higher
          self.assertGreater(probs[1, :].max(),      warped_prob_smooth[1, :].max()	)
          self.assertLess(probs[1, :].min(),      warped_prob_smooth[1, :].min()	)
       def  snake_case_	(      self	)					->		Optional[Any]:
          UpperCamelCase   :  Dict						     =     None
          UpperCamelCase   :  Any						     =     10
          UpperCamelCase   :  Any						     =     2

          # create ramp distribution
          UpperCamelCase   :  List[Any]						     =     np.broadcast_to(np.arange(SCREAMING_SNAKE_CASE_	)[None, :],      (batch_size, vocab_size)	).copy()
          UpperCamelCase   :  Tuple						     =     ramp_logits[1:, : vocab_size // 2] + vocab_size

          UpperCamelCase   :  Dict						     =     FlaxTopKLogitsWarper(3	)

          UpperCamelCase   :  Tuple						     =     top_k_warp(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)

          # check that correct tokens are filtered
          self.assertListEqual(jnp.isinf(scores[0]	).tolist(),      7 * [True] + 3 * [False]	)
          self.assertListEqual(jnp.isinf(scores[1]	).tolist(),      2 * [True] + 3 * [False] + 5 * [True]	)

          # check special case
          UpperCamelCase   :  Optional[int]						     =     5
          UpperCamelCase   :  Optional[int]						     =     FlaxTopKLogitsWarper(top_k=1,      filter_value=0.0,      min_tokens_to_keep=3	)

          UpperCamelCase   :  Union[str, Any]						     =     np.broadcast_to(np.arange(SCREAMING_SNAKE_CASE_	)[None, :],      (batch_size, length)	).copy()
          UpperCamelCase   :  List[str]						     =     top_k_warp_safety_check(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)

          # min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
          self.assertListEqual((scores == 0.0).sum(axis=-1	).tolist(),      [2, 2]	)
       def  snake_case_	(      self	)					->		Union[str, Any]:
          UpperCamelCase   :  int						     =     None
          UpperCamelCase   :  List[str]						     =     10
          UpperCamelCase   :  Optional[Any]						     =     2

          # create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
          UpperCamelCase   :  Optional[int]						     =     np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]]	)	)

          UpperCamelCase   :  Optional[Any]						     =     FlaxTopPLogitsWarper(0.8	)
          UpperCamelCase   :  int						     =     np.exp(top_p_warp(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)	)

          # dist should be filtered to keep min num values so that sum is >= top_p
          # exp (-inf) => 0
          UpperCamelCase   :  Any						     =     np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]]	)
          self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      atol=1e-3	)	)

          # check edge cases with negative and extreme logits
          UpperCamelCase   :  Optional[Any]						     =     np.broadcast_to(np.arange(SCREAMING_SNAKE_CASE_	)[None, :],      (batch_size, vocab_size)	).copy() - (
              vocab_size // 2
          )

          # make ramp_logits more extreme
          UpperCamelCase   :  Tuple						     =     ramp_logits[1] * 1_00.0

          # make sure at least 2 tokens are kept
          UpperCamelCase   :  int						     =     FlaxTopPLogitsWarper(0.9,      min_tokens_to_keep=2,      filter_value=0.0	)
          UpperCamelCase   :  List[str]						     =     top_p_warp(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)

          # first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
          self.assertListEqual((filtered_dist != 0.0).sum(axis=-1	).tolist(),      [3, 2]	)
       def  snake_case_	(      self	)					->		List[Any]:
          UpperCamelCase   :  Union[str, Any]						     =     20
          UpperCamelCase   :  Union[str, Any]						     =     4
          UpperCamelCase   :  Optional[int]						     =     0

          UpperCamelCase   :  Dict						     =     FlaxMinLengthLogitsProcessor(min_length=10,      eos_token_id=SCREAMING_SNAKE_CASE_	)

          # check that min length is applied at length 5
          UpperCamelCase   :  List[str]						     =     ids_tensor((batch_size, 20),      vocab_size=20	)
          UpperCamelCase   :  Any						     =     5
          UpperCamelCase   :  Tuple						     =     self._get_uniform_logits(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Tuple						     =     min_dist_processor(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)
          self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist(),      4 * [-float('inf'	)]	)

          # check that min length is not applied anymore at length 15
          UpperCamelCase   :  Any						     =     self._get_uniform_logits(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Union[str, Any]						     =     15
          UpperCamelCase   :  str						     =     min_dist_processor(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)
          self.assertFalse(jnp.isinf(SCREAMING_SNAKE_CASE_	).any()	)
       def  snake_case_	(      self	)					->		Dict:
          UpperCamelCase   :  str						     =     20
          UpperCamelCase   :  List[Any]						     =     4
          UpperCamelCase   :  List[str]						     =     0

          UpperCamelCase   :  int						     =     FlaxForcedBOSTokenLogitsProcessor(bos_token_id=SCREAMING_SNAKE_CASE_	)

          # check that all scores are -inf except the bos_token_id score
          UpperCamelCase   :  Any						     =     ids_tensor((batch_size, 1),      vocab_size=20	)
          UpperCamelCase   :  List[Any]						     =     1
          UpperCamelCase   :  Tuple						     =     self._get_uniform_logits(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Optional[Any]						     =     logits_processor(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)
          self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :]	).all()	)
          self.assertListEqual(scores[:, bos_token_id].tolist(),      4 * [0]	)  # score for bos_token_id shold be zero

          # check that bos_token_id is not forced if current length is greater than 1
          UpperCamelCase   :  Dict						     =     3
          UpperCamelCase   :  str						     =     self._get_uniform_logits(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  int						     =     logits_processor(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)
          self.assertFalse(jnp.isinf(SCREAMING_SNAKE_CASE_	).any()	)
       def  snake_case_	(      self	)					->		List[str]:
          UpperCamelCase   :  Union[str, Any]						     =     20
          UpperCamelCase   :  Optional[Any]						     =     4
          UpperCamelCase   :  List[Any]						     =     0
          UpperCamelCase   :  int						     =     5

          UpperCamelCase   :  Tuple						     =     FlaxForcedEOSTokenLogitsProcessor(max_length=SCREAMING_SNAKE_CASE_,      eos_token_id=SCREAMING_SNAKE_CASE_	)

          # check that all scores are -inf except the eos_token_id when max_length is reached
          UpperCamelCase   :  str						     =     ids_tensor((batch_size, 4),      vocab_size=20	)
          UpperCamelCase   :  Tuple						     =     4
          UpperCamelCase   :  Union[str, Any]						     =     self._get_uniform_logits(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Any						     =     logits_processor(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)
          self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :]	).all()	)
          self.assertListEqual(scores[:, eos_token_id].tolist(),      4 * [0]	)  # score for eos_token_id should be zero

          # check that eos_token_id is not forced if max_length is not reached
          UpperCamelCase   :  str						     =     3
          UpperCamelCase   :  List[Any]						     =     self._get_uniform_logits(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Union[str, Any]						     =     logits_processor(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)
          self.assertFalse(jnp.isinf(SCREAMING_SNAKE_CASE_	).any()	)
       def  snake_case_	(      self	)					->		int:
          UpperCamelCase   :  int						     =     4
          UpperCamelCase   :  Tuple						     =     10
          UpperCamelCase   :  str						     =     15
          UpperCamelCase   :  List[str]						     =     2
          UpperCamelCase   :  Any						     =     1
          UpperCamelCase   :  List[str]						     =     15

          # dummy input_ids and scores
          UpperCamelCase   :  Dict						     =     ids_tensor((batch_size, sequence_length),      SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  List[Any]						     =     input_ids.copy()

          UpperCamelCase   :  Any						     =     self._get_uniform_logits(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Any						     =     scores.copy()

          # instantiate all dist processors
          UpperCamelCase   :  List[str]						     =     FlaxTemperatureLogitsWarper(temperature=0.5	)
          UpperCamelCase   :  Optional[Any]						     =     FlaxTopKLogitsWarper(3	)
          UpperCamelCase   :  Optional[int]						     =     FlaxTopPLogitsWarper(0.8	)

          # instantiate all logits processors
          UpperCamelCase   :  Optional[Any]						     =     FlaxMinLengthLogitsProcessor(min_length=10,      eos_token_id=SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Dict						     =     FlaxForcedBOSTokenLogitsProcessor(bos_token_id=SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  List[Any]						     =     FlaxForcedEOSTokenLogitsProcessor(max_length=SCREAMING_SNAKE_CASE_,      eos_token_id=SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  List[str]						     =     10

          # no processor list
          UpperCamelCase   :  Any						     =     temp_dist_warp(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Dict						     =     top_k_warp(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Dict						     =     top_p_warp(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Dict						     =     min_dist_proc(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Union[str, Any]						     =     bos_dist_proc(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  List[str]						     =     eos_dist_proc(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)

          # with processor list
          UpperCamelCase   :  List[str]						     =     FlaxLogitsProcessorList(
              [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]	)
          UpperCamelCase   :  Optional[Any]						     =     processor(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)

          # scores should be equal
          self.assertTrue(jnp.allclose(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      atol=1e-3	)	)

          # input_ids should never be changed
          self.assertListEqual(input_ids.tolist(),      input_ids_comp.tolist()	)






       def  snake_case_	(      self	)					->		int:
          UpperCamelCase   :  Optional[Any]						     =     4
          UpperCamelCase   :  Tuple						     =     10
          UpperCamelCase   :  Union[str, Any]						     =     15
          UpperCamelCase   :  Union[str, Any]						     =     2
          UpperCamelCase   :  Optional[Any]						     =     1
          UpperCamelCase   :  int						     =     15

          # dummy input_ids and scores
          UpperCamelCase   :  Dict						     =     ids_tensor((batch_size, sequence_length),      SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  str						     =     input_ids.copy()

          UpperCamelCase   :  Optional[int]						     =     self._get_uniform_logits(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Union[str, Any]						     =     scores.copy()

          # instantiate all dist processors
          UpperCamelCase   :  Dict						     =     FlaxTemperatureLogitsWarper(temperature=0.5	)
          UpperCamelCase   :  Optional[Any]						     =     FlaxTopKLogitsWarper(3	)
          UpperCamelCase   :  Union[str, Any]						     =     FlaxTopPLogitsWarper(0.8	)

          # instantiate all logits processors
          UpperCamelCase   :  str						     =     FlaxMinLengthLogitsProcessor(min_length=10,      eos_token_id=SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Any						     =     FlaxForcedBOSTokenLogitsProcessor(bos_token_id=SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Optional[int]						     =     FlaxForcedEOSTokenLogitsProcessor(max_length=SCREAMING_SNAKE_CASE_,      eos_token_id=SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  Any						     =     10

          # no processor list
          def run_no_processor_list(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	):
             UpperCamelCase   :  Optional[Any]						     =     temp_dist_warp(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)
             UpperCamelCase   :  str						     =     top_k_warp(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)
             UpperCamelCase   :  Any						     =     top_p_warp(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)
             UpperCamelCase   :  List[str]						     =     min_dist_proc(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)
             UpperCamelCase   :  List[str]						     =     bos_dist_proc(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)
             UpperCamelCase   :  List[str]						     =     eos_dist_proc(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)
             return scores

          # with processor list
          def run_processor_list(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	):
             UpperCamelCase   :  Tuple						     =     FlaxLogitsProcessorList(
                 [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]	)
             UpperCamelCase   :  Union[str, Any]						     =     processor(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)
             return scores

          UpperCamelCase   :  Dict						     =     jax.jit(SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  List[str]						     =     jax.jit(SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  int						     =     jitted_run_no_processor_list(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  int						     =     jitted_run_processor_list(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)

          # scores should be equal
          self.assertTrue(jnp.allclose(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      atol=1e-3	)	)

          # input_ids should never be changed
          self.assertListEqual(input_ids.tolist(),      input_ids_comp.tolist()	)



